In my latest blog for Ingentis' Innovation Blog, I explore the relationship between an HR practitioner and data.
What is meant by HR data?
‘All this to say, change is coming, and it’s best to get a head start’. This is a great quote in a Harvard Business Review article, ‘21 HR Jobs of the Future’, by Jeanne C. Meister and Robert H. Brown published August 2020, and which succinctly anticipates the importance of data in the HR function of the future. This is not to say that HR data is not important at this point in time, it has been and continues to be important. My point is that HR data will be critically important - research shows that it will fundamentally change the nature of the role of the HR practitioner.
What is HR data? HR data refers to a wide variety of information related to people in an organisation. It includes quantitative and qualitative information that HR departments collect, manage, and analyse and to support decision-making.
HR data can be found in systems in HR, IT and other departments. It can also be found in external sources such as salary surveys and labour workforce reports. Some common types of HR data include:
Employee Demographics: Basic personal information such as age, gender, marital status, and ethnicity, often used to manage diversity and inclusion.
Recruitment Data: Information about job applicants, such as application and CV data, interview scores, helps in assessing the effectiveness of recruiting activities.
Attendance and Leave Data: Data on working hours, attendance, holiday, sick absence, and other leave absences helping in tracking employee availability, spot trends or patterns, and ensuring compliance with leave policies.
Reward and Benefits: Information on salaries, bonuses, benefits, and incentives, allowing HR to manage payroll and design competitive reward packages.
Performance Data: Employee reviews, performance metrics, goals, feedback, and appraisals, which support tracking productivity and identifying areas for improvement.
Training and Development: Data on skills, certifications, training completed, and learning progress, helping identify skill gaps and ensuring employees have the necessary competencies.
Employee Engagement and Satisfaction: Feedback, survey results, and staff satisfaction analysis that provide insight into employee morale, motivation, and potential retention issues.
Compliance and Safety Data: Information on certifications, health and safety training, and compliance with industry regulations to ensure legal compliance and a safe work environment.
HR data needs to be analysed to gain insights into people in an organisation - to spot patterns and trends which can inform actionable recommendations, for example, to forecast future workforce needs.
In recent years, technology has developed the tools to enable HR functions to analyse their data and thus become more strategic. HR data analysis requires knowledge of statistical analysis, interpretation and presentation of data in a meaningful way. However, this shift from the typical HR practitioner role presents a challenge for the HR profession.
Does the HR function have the skills and capability to analyse data? Can the typical HR practitioner pivot into data analysis, or does the HR function hire a data analyst or scientist?
The emerging HR roles focusing on data
The Cognizant Center for Future of Work and Future Workplace jointly embarked on an initiative to determine exactly what the future of HR will look like. The initiative brought together a network of nearly 100 CHROs, CLOs, and VP’s of talent and workforce transformation to envision how HR’s role might evolve over the next 10 years (Meister J.C, and Brown R.H, 21 HR Jobs of the Future, Harvard Business Review, 2020). Each job was ranked by its organisational impact narrowing down the list to 21 HR Jobs of the Future. Some of the roles are entirely new positions, others are new responsibilities that are becoming increasingly important. All 21 jobs embody five core themes:
1. Individual and organizational resilience
2. Organizational trust and safety
3. Creativity and innovation
4. Data literacy
5. Human-machine partnerships
The 21 HR Jobs of the Future were arranged on a 2×2 grid (see Figure 1); the X-axis depicts time, and the order in which it is expect the roles will appear over the next 10 years, while the y-axis depicts “technology centricity”.
Figure 1: 21 HR Jobs of the Future
Let’s take a closer look at one of the five core themes - data literacy...
As an HR practitioner or function, to what extent are you analysing HR data to solve people issues? I was interested to see data literacy on the list. I think HR will increasingly undertake data analysis, providing the HR profession can pivot to becoming a data-driven function. In doing so HR ‘gains a seat at the table’ to coin an often said phrase - which means that HR are included in high-level decision-making conversions. HR is not only invited, but their input is valued and appreciated.
HR Data Detective
How does this role sound to you? You might feel alarmed by the title of the job but if you think about it, it makes sense. HR data analysis will require greater data literacy skills in the HR function. The HR Data Detective - one of the 21 HR Jobs of the Future - could help to bring about the shift. This person would be responsible for synthesising disparate data streams to help solve business problems. Equally comfortable with being “in the weeds” of data as well as seeing and explaining “the big picture”, HR data detectives would gather and compile insights to help improve employee performance and drive better results for the whole business (Meister J.C, and Brown R.H, 21 HR Jobs of the Future, Harvard Business Review, 2020).
The HR data detective role is a natural progression from the HR management information role. This person also uses data about people to highlight a problem but does not necessarily investigate the root cause of the problem.
The role of HR data
Marler and Boudreau (International Journal of Human Resource Management, An evidence-based review of HR Analytics, 2016) define HR data analytics as ‘a number of processes, enabled by technology, that use descriptive, visual and statistical methods to interpret people data and HR processes'. These processes include:
Level 1 Descriptive analytics: Describes a particular period or a historical trend. For example, employee turnover, sickness absence rates, starters and leavers and ‘lost time’ rate due to absence.
Level 2 Descriptive analytics using multidimensional data: Combines different types of data to investigate a specific idea. Like combining leadership capability data with engagement scores to measure leadership effectiveness.
Level 3 Predictive analytics: Uses data to predict what might happen. For example, looking at historical workforce data and external labour market trends and using these to build a model that predicts the organisation’s future workforce needs.
Level 4 Prescriptive analytics: Uses the results of descriptive and predictive analytics to immediately recommend actions.
How far does your HR data analytics process go? Level 1, 2, 3 or 4? To be a true HR Data Detective, you would need to operate at Level 3 and 4.
The relationship between an HR practitioner and data
HR practitioners and data have a complicated relationship. The HR profession has a wide-ranging role and expertise but ultimately good HR is about shaping good work and good working lives within organisations. I started off my career in HR because I was interested in the psychology and behaviour of people in the workplace. I think a lot of people who work in the HR profession do so for similar reasons. The HR profession tends to attract people that want to create successful organisations and make a real difference to people’s lives. This gives HR practitioners a strong sense of purpose.
It is then not immediately obvious that data plays an important role in the HR function. For some HR functions, gathering and reporting data at Level 1 of Marler and Boudreau’s maturity of HR data analytics is where most HR functions comfortably operate.
The Chartered Institute of Personnel (CIPD) Profession Map puts HR data analytics as one of the ‘specialist knowledge’ areas in the HR profession alongside the typical HR activities like learning and development, employee relations and resourcing.
Figure 2: CIPD Profession Map
The specialist knowledge standards describe the knowledge an HR practitioner needs to be expert in using data about people and the organisation to inform decision-making. This knowledge is what I call the HR Data Detective Persona. This person has an understanding of…
Data. How to responsibly use HR data to generate insights about people.
Data technology and platforms. How to integrate HR data from disparate data sources or databases and automate data flow between the sources.
Analytical consulting. How to curate HR data to give strategic advice which shapes solutions to people issues.
Research design. How to conduct research design and use qualitative and quantitative techniques to address issues.
Data analysis. How to identify complex patterns in HR data to generate insights into people issues.
Data science. How to interpret and apply complex HR data modelling to people issues.
Data visualization. How to use HR data visualisation to influence decision-making on people issues.
But just how easy is it for an HR practitioner to pivot to HR data analytics? Well it is quite a shift for the HR practitioner but it is possible. It is likely that the HR Data Detective talent pool will include people with advanced data literacy skills, for example, in data science or AI. The HR function could also acquire data literacy skills from the early careers talent pools such as interns, apprentices and graduates.
I think HR practitioners can pivot to the role but they will need to upskill themselves in data analysis, data science and data visualisation, specifically, how to influence and shape business strategy through insights from HR data, how to interpret business needs and provide data modes through self-service dashboards and how to innovate and use leading practice in data visualisation to influence decision-making.
There are lots of emerging new roles in the HR profession as shown in the 21 HR Jobs of the Future - there is always the possibility for the HR Practitioner and HR Data Detective to collaborate instead and to share their mutual expertise.
Using Ingentis org.manager as a data screening tool
Do you see yourself as an HR Data Detective? Why not start with Ingentis’ Data Quality Screening extension. It is important that HR data is accurate and reliable for precise reporting and meaningful dashboards. The Data Quality Screening extension is a useful tool which helps an HR Data Detective to quickly identify errors or gaps in data and systematically improve data quality and integrity throughout the organisation. HR can therefore carry out more reliable analyses and make more informed decisions.
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Nicholas Toko is a freelance HR and organisational effectiveness consultant and a Jungian Analyst-in-training. He is an expert on the bridge between organisational strategy, structure, culture, people, process and technology including artificial intelligence (AI), Enterprise Resource Planning (ERP) systems and the application of analytical psychology in a psychosocial context including the workplace, specifically to analyse individuals, teams and organisations in-depth and in his psychoanalytic therapy practice for individuals. Follow or connect with him on LinkedIn at www.linkedin.com/in/nicholastoko
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